AI Usage Tracker
ACTIVEOne local dashboard for Claude and Codex spend
Launched July 21, 2026
About AI Usage Tracker
AI Usage Tracker is a 100 % local‑first dashboard that automatically discovers Claude and Codex usage across more than ten developer tools. It visualises daily spend, peak‑hour heatmaps, model‑tier pricing and project‑level token costs without sending any data to the cloud.
Pricing
Pricing information isn't available yet.
Capabilities
Local‑first operation
Runs entirely on the user’s laptop, reading files locally with no accounts, API keys, telemetry or servers involved.
Auto‑discovery of Claude & Codex usage
Scans common directories (e.g., ~/.claude, ~/.cursor, ~/.aider) to identify sessions from tools such as Claude Code, Cursor, Windsurf, Cline, Aider, Codex CLI, and others.
Spend visualisation
Displays daily spend and cost breakdowns per tool using stacked bar charts powered by Chart.js.
Peak‑hours heatmap
Shows when AI usage spikes, highlighting costly periods like overnight runs.
Smart projections
Provides yesterday’s delta, monthly forecasts and highlights the most expensive session.
Powerful filtering
Filters data by source, model, date range, or minimum cost with visual chips for quick insight.
Projects view
Aggregates sessions by working directory, allowing users to see which projects consume the most tokens.
Session detail drill‑down
Offers per‑session breakdowns, conversation previews, token counts and a one‑click command to resume a Claude session.
Use Cases
Developer cost monitoring
Track and visualise how much Claude and Codex usage costs per day, per project, and per model to stay within budget.
For: Software developers and engineers who regularly use AI coding assistants.
Budget forecasting for AI spend
Use smart projections and monthly forecasts to anticipate future AI expenses and plan resource allocation.
For: Tech leads, project managers, and finance teams overseeing AI‑related budgets.
Identifying expensive AI sessions
Highlight the most costly sessions and peak usage times to optimise prompts or switch to cheaper model tiers.
For: AI‑savvy developers and DevOps engineers looking to reduce token costs.
Project‑level cost attribution
Map AI token consumption to specific codebases or projects, enabling cost accountability across teams.
For: Engineering managers and team leads managing multiple projects.
Privacy‑focused usage analytics
Gain detailed spend insights while keeping all data on‑device, meeting compliance or data‑privacy requirements.
For: Organizations with strict data‑security policies, security‑conscious developers.